{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Selecting the Best Model (ML Practices).ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOdXyD9we3koiZH+RtJHqHf"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y3B8NzYeWrHG"
      },
      "source": [
        "#Important Notes/Steps during ML workflows."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2hMBJ3OWIxP"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CEzaIYKpXVYT"
      },
      "source": [
        "1. Adding an extra dimension to array."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3g547TNiW3kd",
        "outputId": "a9cf417e-3b39-410b-8a62-2c5077b0c7d0"
      },
      "source": [
        "X = np.linspace(0,10)\n",
        "X.shape"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6HU0S7qXTnB"
      },
      "source": [
        "X = X[:,np.newaxis]"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-2akCtAAXeld",
        "outputId": "8fe2e58a-567d-4a9b-87f1-8b61b8eebdb9"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mrPqMW5jXlaC"
      },
      "source": [
        "#This is important since the Feature Matrix must always be 2D."
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ewNZiKA47qjG"
      },
      "source": [
        "# Validation Curves in SK-Learn.\n",
        "\n",
        "> Important Learning : How to fit complex/high order polynomials in SK-Learn."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7_ZM_wJXsn-"
      },
      "source": [
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.pipeline import make_pipeline"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1Qsy97M8Grx"
      },
      "source": [
        "def PolyReg(degree=2, **kwargs):\n",
        "\n",
        "  P = make_pipeline(PolynomialFeatures(degree),\n",
        "                    LinearRegression(**kwargs))\n",
        "  \n",
        "  return P\n",
        "\n",
        "\n",
        "def datamaker( N=20,e=1):\n",
        "\n",
        "  X = np.random.rand(N,1)**2\n",
        "\n",
        "  #ravel is flattening.\n",
        "  y = 10 - 1/(X.ravel() + 0.1)\n",
        "\n",
        "  if e>0:\n",
        "    y+=  e*np.random.randn(N)\n",
        "\n",
        "  return X,y\n"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StbA9nDQ9hQm"
      },
      "source": [
        "X_train , y_train = datamaker(100,0.5)"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MAXoORl0Diih"
      },
      "source": [
        "### Fitting Polynomials of Different Degrees."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aLfhVRnWA5PT"
      },
      "source": [
        "#Let's go with three models. \n",
        "#Model 1 = Degree 1\n",
        "\n",
        "m1 = PolyReg(1).fit(X_train, y_train)\n",
        "\n",
        "#Model 2 = Degree 3\n",
        "m3 = PolyReg(3).fit(X_train,y_train)\n",
        "\n",
        "#Model 3 = Degree 5\n",
        "m5 = PolyReg(5).fit(X_train,y_train)"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GHDoyI5vDhai"
      },
      "source": [
        "#Test Data. \n",
        "X_test = np.linspace(0,1,100).reshape(100,1)"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X7Vsy59oEDfN"
      },
      "source": [
        "#Predictions\n",
        "\n",
        "\n",
        "\n",
        "yp1 = m1.predict(X_test) \n",
        "yp3 = m3.predict(X_test)\n",
        "yp5 = m5.predict(X_test)"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "lJomIe94EFGN",
        "outputId": "d10d4ec3-2e50-4446-8190-651258af1879"
      },
      "source": [
        "#Plotting\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.style.use('seaborn')\n",
        "\n",
        "\n",
        "plt.grid()\n",
        "plt.scatter(X_train.ravel() , y_train, color='black')\n",
        "plt.plot(X_test.ravel() , yp1 , lw=2, label='degree = 1')\n",
        "plt.plot(X_test.ravel() , yp3 , lw=2, label='degree = 3')\n",
        "plt.plot(X_test.ravel() , yp5 , lw=2, label='degree = 5')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "plt.title('Regression with Higher Power Polynomials')\n",
        "plt.legend()\n",
        "plt.grid()"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YhR_QxpnOO1i"
      },
      "source": [
        "#Now let's plot training and validation curves over various degrees. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MJaPhEmEPf7"
      },
      "source": [
        "from sklearn.model_selection import validation_curve, learning_curve, GridSearchCV"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybhXKp0SG0O5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "762fb06b-d4a1-415c-c5ec-f4d5f286c143"
      },
      "source": [
        "degree = np.arange(1,15)\n",
        "\n",
        "\n",
        "X,y = datamaker(200)\n",
        "train_score , val_score = validation_curve(PolyReg(), X, y,\n",
        "                              'polynomialfeatures__degree',degree, cv=7)\n",
        "\n",
        "tsm = [] ; vsm = []\n",
        "for i in range(train_score.shape[0]):\n",
        "\n",
        "  tsm.append(np.mean(train_score[i,:]))\n",
        "  vsm.append(np.mean(val_score[i,:]))\n",
        "\n",
        "\n"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8337659cd0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "uTDaNu95sn6V",
        "outputId": "1f08514a-17e0-4d97-f0db-855b18c45b8a"
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "plt.plot(degree,tsm, label='Training Score')\n",
        "plt.plot(degree,vsm, color='red',label='Validation Score')\n",
        "\n",
        "\n",
        "plt.axvline(x=4,color='k', label='Best Model')\n",
        "\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.xlabel('Degree/Model Complexity')\n",
        "plt.ylabel('Performance Score')\n",
        "\n",
        "\n"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Performance Score')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7X2MTFlj1u69"
      },
      "source": [
        "#The best model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZASCwzVUtE5d"
      },
      "source": [
        "m4 = PolyReg(4).fit(X_train,y_train)\n",
        "\n",
        "yp4 = m4.predict(X_test)"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "u74kbOLgtxyb",
        "outputId": "2cecce3c-9b11-451a-c949-ea754b3b20bc"
      },
      "source": [
        "#Plotting\n",
        "plt.figure(figsize=(12,6))\n",
        "plt.style.use('seaborn')\n",
        "\n",
        "\n",
        "plt.grid()\n",
        "plt.scatter(X_train.ravel() , y_train, color='black')\n",
        "# plt.plot(X_test.ravel() , yp1 , lw=2, label='degree = 1')\n",
        "# plt.plot(X_test.ravel() , yp3 , lw=2, label='degree = 3')\n",
        "# plt.plot(X_test.ravel() , yp5 , lw=2, label='degree = 5')\n",
        "plt.plot(X_test.ravel() , yp4 , lw=2,color='red', label='degree = 4')\n",
        "\n",
        "\n",
        "\n",
        "plt.title('Regression with Higher Power Polynomials')\n",
        "plt.legend()\n",
        "plt.grid()"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUs9idMf2Fp0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}